Physical sensors are widely used in many products, such as modern machines, to measure and monitor physical phenomena, such as temperature, speed, and emissions from motor vehicles. Physical sensors often take direct measurements of the physical phenomena and convert these measurements into measurement data to be further processed by control systems. Although physical sensors take direct measurements of the physical phenomena, physical sensors and associated hardware are often costly and, sometimes, unreliable. Further, when control systems rely on physical sensors to operate properly, a failure of a physical sensor may render such control systems inoperable. For example, the failure of an intake manifold pressure sensor in an engine may result in shutdown of the engine entirely even if the engine itself is still operable.
Instead of direct measurements, virtual sensors have been developed to process other various physically measured values and to produce values that were previously measured directly by physical sensors. For example, U.S. Pat. No. 5,386,373 (the '373 patent) issued to Keeler et al. on Jan. 31, 1995, discloses a virtual continuous emission monitoring system with sensor validation. The '373 patent uses a back propagation-to-activation model and a monte-carlo search technique to establish and optimize a computational model used for the virtual sensing system to derive sensing parameters from other measured parameters.
A modern machine may need multiple sensors to function properly, and multiple virtual sensors may be used. However, conventional multiple virtual sensors are often used independently without taking into account other virtual sensors in an operating environment, which may result in undesired results. For example, multiple virtual sensors may compete for limited computing resources, such as processor, memory, or I/O, etc. An output of one virtual sensor model could also inadvertently becomes an input to another virtual sensor model, which can result in unpredictable effects in complex control systems relying on these values. Further, other types of interactions among the multiple virtual sensors may cause undesired or unpredictable results, such as feedback loops or transient control instabilities.
Further, conventional multiple virtual sensors are often incapable of being calibrated to provide users information such as sensing range, uncertainty, and sensing conditions, etc., about the multiple virtual sensors and, more specifically, the multiple virtual sensors as a whole.
Methods and systems consistent with certain features of the disclosed systems are directed to solving one or more of the problems set forth above.